

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__a    :Dict      =			logging.get_logger(__name__)

class   _a					(			snake_case_				):




      """simple docstring"""

      _lowerCamelCase       :			List[Any]      		=       ['pixel_values']





      def __init__(   self							:				Dict						,			UpperCAmelCase							:				bool = True						,			UpperCAmelCase							:				Optional[Dict[str, int]] = None						,			UpperCAmelCase							:				PILImageResampling = PILImageResampling.BICUBIC						,			UpperCAmelCase							:				bool = True						,			UpperCAmelCase							:				bool = True						,			UpperCAmelCase							:				Union[int, float] = 1 / 255						,			UpperCAmelCase							:				Dict[str, int] = None						,			UpperCAmelCase							:				bool = True						,			UpperCAmelCase							:				Optional[Union[float, List[float]]] = None						,			UpperCAmelCase							:				Optional[Union[float, List[float]]] = None						,			**UpperCAmelCase							:				Optional[int]						,			):
            super().__init__(**UpperCAmelCase	)
            A_						  =				size if size is not None else {"height": 224, "width": 224}
            A_						  =				get_size_dict(UpperCAmelCase	)
            A_						  =				crop_size if crop_size is not None else {"height": 224, "width": 224}
            A_						  =				get_size_dict(UpperCAmelCase						,			default_to_square=UpperCAmelCase						,			param_name="crop_size"	)

            A_						  =				do_resize
            A_						  =				do_rescale
            A_						  =				do_normalize
            A_						  =				do_center_crop
            A_						  =				crop_size
            A_						  =				size
            A_						  =				resample
            A_						  =				rescale_factor
            A_						  =				image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
            A_						  =				image_std if image_std is not None else IMAGENET_DEFAULT_STD





      def 		__A	(   self							:				Any						,			UpperCAmelCase							:				np.ndarray						,			UpperCAmelCase							:				Dict[str, int]						,			UpperCAmelCase							:				PILImageResampling = PILImageResampling.BILINEAR						,			UpperCAmelCase							:				Optional[Union[str, ChannelDimension]] = None						,			**UpperCAmelCase							:				Optional[int]						,			):
            A_						  =				get_size_dict(UpperCAmelCase	)

            if "shortest_edge" in size:
                  A_						  =				get_resize_output_image_size(UpperCAmelCase						,			size=size["shortest_edge"]						,			default_to_square=UpperCAmelCase	)
                  # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
            elif "height" in size and "width" in size:
                  A_						  =				(size["height"], size["width"])
            else:
                  raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''	)
            return resize(UpperCAmelCase						,			size=UpperCAmelCase						,			resample=UpperCAmelCase						,			data_format=UpperCAmelCase						,			**UpperCAmelCase	)





      def 		__A	(   self							:				str						,			UpperCAmelCase							:				np.ndarray						,			UpperCAmelCase							:				Dict[str, int]						,			UpperCAmelCase							:				Optional[Union[str, ChannelDimension]] = None						,			**UpperCAmelCase							:				Optional[Any]						,			):
            A_						  =				get_size_dict(UpperCAmelCase	)
            if "height" not in size or "width" not in size:
                  raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''	)
            return center_crop(UpperCAmelCase						,			size=(size["height"], size["width"])						,			data_format=UpperCAmelCase						,			**UpperCAmelCase	)





      def 		__A	(   self							:				str						,			UpperCAmelCase							:				np.ndarray						,			UpperCAmelCase							:				float						,			UpperCAmelCase							:				Optional[Union[str, ChannelDimension]] = None						,			**UpperCAmelCase							:				Any	):
            return rescale(UpperCAmelCase						,			scale=UpperCAmelCase						,			data_format=UpperCAmelCase						,			**UpperCAmelCase	)





      def 		__A	(   self							:				List[str]						,			UpperCAmelCase							:				np.ndarray						,			UpperCAmelCase							:				Union[float, List[float]]						,			UpperCAmelCase							:				Union[float, List[float]]						,			UpperCAmelCase							:				Optional[Union[str, ChannelDimension]] = None						,			**UpperCAmelCase							:				Dict						,			):
            return normalize(UpperCAmelCase						,			mean=UpperCAmelCase						,			std=UpperCAmelCase						,			data_format=UpperCAmelCase						,			**UpperCAmelCase	)







      def 		__A	(   self							:				Optional[int]						,			UpperCAmelCase							:				ImageInput						,			UpperCAmelCase							:				Optional[bool] = None						,			UpperCAmelCase							:				Dict[str, int] = None						,			UpperCAmelCase							:				PILImageResampling = None						,			UpperCAmelCase							:				bool = None						,			UpperCAmelCase							:				int = None						,			UpperCAmelCase							:				Optional[bool] = None						,			UpperCAmelCase							:				Optional[float] = None						,			UpperCAmelCase							:				Optional[bool] = None						,			UpperCAmelCase							:				Optional[Union[float, List[float]]] = None						,			UpperCAmelCase							:				Optional[Union[float, List[float]]] = None						,			UpperCAmelCase							:				Optional[Union[str, TensorType]] = None						,			UpperCAmelCase							:				Union[str, ChannelDimension] = ChannelDimension.FIRST						,			**UpperCAmelCase							:				str						,			):
            A_						  =				do_resize if do_resize is not None else self.do_resize
            A_						  =				do_rescale if do_rescale is not None else self.do_rescale
            A_						  =				do_normalize if do_normalize is not None else self.do_normalize
            A_						  =				do_center_crop if do_center_crop is not None else self.do_center_crop
            A_						  =				crop_size if crop_size is not None else self.crop_size
            A_						  =				get_size_dict(UpperCAmelCase						,			param_name="crop_size"						,			default_to_square=UpperCAmelCase	)
            A_						  =				resample if resample is not None else self.resample
            A_						  =				rescale_factor if rescale_factor is not None else self.rescale_factor
            A_						  =				image_mean if image_mean is not None else self.image_mean
            A_						  =				image_std if image_std is not None else self.image_std

            A_						  =				size if size is not None else self.size
            A_						  =				get_size_dict(UpperCAmelCase	)

            if not is_batched(UpperCAmelCase	):
                  A_						  =				[images]

            if not valid_images(UpperCAmelCase	):
                  raise ValueError(
                      "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                      "torch.Tensor, tf.Tensor or jax.ndarray."	)

            if do_resize and size is None:
                  raise ValueError("Size must be specified if do_resize is True."	)

            if do_center_crop and crop_size is None:
                  raise ValueError("Crop size must be specified if do_center_crop is True."	)

            if do_rescale and rescale_factor is None:
                  raise ValueError("Rescale factor must be specified if do_rescale is True."	)

            # All transformations expect numpy arrays.
            A_						  =				[to_numpy_array(UpperCAmelCase	) for image in images]

            if do_resize:
                  A_						  =				[self.resize(image=UpperCAmelCase						,			size=UpperCAmelCase						,			resample=UpperCAmelCase	) for image in images]

            if do_center_crop:
                  A_						  =				[self.center_crop(image=UpperCAmelCase						,			size=UpperCAmelCase	) for image in images]

            if do_rescale:
                  A_						  =				[self.rescale(image=UpperCAmelCase						,			scale=UpperCAmelCase	) for image in images]

            if do_normalize:
                  A_						  =				[self.normalize(image=UpperCAmelCase						,			mean=UpperCAmelCase						,			std=UpperCAmelCase	) for image in images]

            A_						  =				[to_channel_dimension_format(UpperCAmelCase						,			UpperCAmelCase	) for image in images]

            A_						  =				{"pixel_values": images}
            return BatchFeature(data=UpperCAmelCase						,			tensor_type=UpperCAmelCase	)



import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
						import jax
						import jax.numpy as jnp
						from flax.jax_utils import replicate
						from flax.training.common_utils import shard







@slow
@require_flax
class 							lowerCamelCase__						(				unittest.TestCase   ):






			"""simple docstring"""






			def 					snake_case__					(				self  ):

						'''simple docstring'''


						super().tearDown()
						gc.collect()




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__,   UpperCamelCase__       =							FlaxControlNetModel.from_pretrained(
						    "lllyasviel/sd-controlnet-canny"   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__,   UpperCamelCase__       =							FlaxStableDiffusionControlNetPipeline.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"   ,    controlnet=snake_case   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__       =							controlnet_params

						UpperCamelCase__       =							"bird"
						UpperCamelCase__       =							jax.device_count()
						UpperCamelCase__       =							pipe.prepare_text_inputs([prompts] * num_samples  )

						UpperCamelCase__       =							load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"  )
						UpperCamelCase__       =							pipe.prepare_image_inputs([canny_image] * num_samples  )

						UpperCamelCase__       =							jax.random.PRNGKey(0  )
						UpperCamelCase__       =							jax.random.split(snake_case   ,    jax.device_count()  )

						UpperCamelCase__       =							replicate(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )

						UpperCamelCase__       =							pipe(
						    prompt_ids=snake_case   ,    image=snake_case   ,    params=snake_case   ,    prng_seed=snake_case   ,    num_inference_steps=50   ,    jit=snake_case   ,    ).images
						assert images.shape == (jax.device_count(), 1, 768, 512, 3)

						UpperCamelCase__       =							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
						UpperCamelCase__       =							images[0, 253:256, 253:256, -1]

						UpperCamelCase__       =							jnp.asarray(jax.device_get(image_slice.flatten()  )  )
						UpperCamelCase__       =							jnp.array(
						    [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]  )
						print(F'''output_slice: {output_slice}'''  )
						assert jnp.abs(output_slice - expected_slice  ).max() < 1E-2







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__,   UpperCamelCase__       =							FlaxControlNetModel.from_pretrained(
						    "lllyasviel/sd-controlnet-openpose"   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__,   UpperCamelCase__       =							FlaxStableDiffusionControlNetPipeline.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"   ,    controlnet=snake_case   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__       =							controlnet_params

						UpperCamelCase__       =							"Chef in the kitchen"
						UpperCamelCase__       =							jax.device_count()
						UpperCamelCase__       =							pipe.prepare_text_inputs([prompts] * num_samples  )

						UpperCamelCase__       =							load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"  )
						UpperCamelCase__       =							pipe.prepare_image_inputs([pose_image] * num_samples  )

						UpperCamelCase__       =							jax.random.PRNGKey(0  )
						UpperCamelCase__       =							jax.random.split(snake_case   ,    jax.device_count()  )

						UpperCamelCase__       =							replicate(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )

						UpperCamelCase__       =							pipe(
						    prompt_ids=snake_case   ,    image=snake_case   ,    params=snake_case   ,    prng_seed=snake_case   ,    num_inference_steps=50   ,    jit=snake_case   ,    ).images
						assert images.shape == (jax.device_count(), 1, 768, 512, 3)

						UpperCamelCase__       =							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
						UpperCamelCase__       =							images[0, 253:256, 253:256, -1]

						UpperCamelCase__       =							jnp.asarray(jax.device_get(image_slice.flatten()  )  )
						UpperCamelCase__       =							jnp.array(
						    [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]  )
						print(F'''output_slice: {output_slice}'''  )
						assert jnp.abs(output_slice - expected_slice  ).max() < 1E-2


